Journal of Statistics: Advances in Theory and ApplicationsAuthor's: Elasma Milanzi, Matthew Spittal and Lyle Gurrin
Pages: [1] - [34]
Received Date: January 31, 2020; Revised March 06, 2020
Submitted by:
DOI: http://dx.doi.org/10.18642/jsata_7100122116
The current interest in meta-analysis of count data in which some studies have zero events (sparse data) has led to re-assessment of commonly used meta-analysis methods to establish their validity in such scenarios. The general consensus is that methods which exclude studies with zero events should be avoided. In the family of parametric methods, random effects models come out highly recommended. While acknowledging the strength of this approach, one of its aspects with potentially undesirable impact on the results, is often overlooked. The random effects approach accounts for the variation in the effect measure across studies by using models with random slopes. It has been shown that parameters associated with a random structure risk being estimated with biased unless the distribution of the random effects is correctly specified. In meta-analysis the parameter of interest, average effect measure, is associated with a random structure (random slope). Information on how the effect measure point and precision estimates are affected by misspecification of random effects distribution is still lacking. To fill in the information gap, we used a simulation study to investigate the impact of misspecification of distribution of random effects in this context and provide guidelines in using the random effects approach. Our results indicated that relative bias in the estimated effect measure could be as high as 30% and 95% confidence interval coverage as low as 0%. These results send a clear message that possible effects of misspecification of the distribution of random effects should not be ignored. In light of these findings, we have proposed a sensitivity analysis that also establishes whether a random slope model is necessary.
meta-analysis, misspecification, random effects, sparse data, bias.
